Do Electrocardiogram Rhythm Findings Predict Cardiac Activity During a Cardiac Arrest? A Study from Sonography in Cardiac Arrest and Hypotension in the Emergency Department (SHoC-ED)
Bibliographic record
Abstract
Introduction Electrocardiographic (ECG) rhythms are used during advanced cardiac life support (ACLS) to guide resuscitation management. Survival to hospital discharge has been reported to be better for patients with pulseless electrical activity (PEA) than asystole in out-of-hospital arrests. Despite this, treatment for these two (non-shockable) rhythms is combined in ACLS guidelines. This study examines if the recorded cardiac rhythm of asystole or PEA during ACLS accurately predicts mechanical cardiac activity as determined by point-of-care ultrasound (PoCUS). Methods A database review was completed for patients (> 19 years without a do not resuscitate (DNR) order) who presented to a tertiary emergency department in PEA or asystolic cardiac arrest between 2010 and 2014. Patients were separated into two groups: those with electrical cardiac activity (PEA) and those without (asystole). We compared ECG rhythm and PoCUS-documented cardiac activity results (both initial and any) for each case. Results A total of 186 patients met the study criteria. The 46 patients with PEA on ECG were more likely to have cardiac activity than the 140 patients with asystole (odds ratio 7.22 (95% confidence intervals 2.79-18.7) for activity on initial PoCUS; odds ratio 5.45 (2.49-12.0) for activity on any PoCUS during arrest). ECG alone was poorly sensitive for initial cardiac activity (63.64%; 40.66% to 82.80%) and any cardiac activity (54.29%; 36.65% to 71.17%), with specificity marginally better at 80.49% (73.59% to 86.25%) for initial and 82.12% (75.06% to 87.87%) for any activity. Conclusion Our results suggest that ECG rhythm alone is not an accurate predictor of cardiac activity. This supports the use of PoCUS during cardiac arrest, in addition to ECG, to identify patients with ongoing mechanical cardiac activity and to help determine appropriate treatment for this group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".